Edge Intelligence in Wearable Healthcare IoT: A Systematic Review

The rapid proliferation of wearable healthcare devices and Internet of Things (IoT) technologies is fundamentally reshaping modern healthcare toward continuous, real-time patient monitoring. Traditional cloud-centric architectures, however, introduce significant challenges including communication latency, bandwidth saturation, and patient data privacy risks. These limitations have catalysed the emergence of edge intelligence — a paradigm where artificial intelligence and data processing are performed proximate to the wearable device rather than in a remote cloud infrastructure. This systematic review synthesises research published between 2019 and 2026 on edge-enabled wearable healthcare IoT systems. The paper presents a comprehensive taxonomy of system architectures, sensing technologies, AI/ML techniques, and communication models employed in wearable health monitoring. A comparative analysis of 70+ peer-reviewed studies highlights the advantages of edge intelligence in latency reduction, privacy preservation, and energy efficiency. Survey trends, open research gaps, and unresolved challenges are critically examined, culminating in a structured future research roadmap for intelligent, patient-centric healthcare ecosystems.